Drowsy driving is a main cause of severe accidents. Drowsiness is responsible for 30% to 37% of fatal road accident in Iran. In this paper, driver drowsiness is detected based on features related to the steering wheel angle and the lateral position of the vehicle. Data from the vehicle and the virtual road are used to extract drowsiness features. Experimental results using a driving simulator are presented. Participants were 21 to 28 year-old males with a high tendency to sleep (Epworth Sleepiness Scale≥10). The subjects had to drive a lane keeping scenario on a long and monotonous virtual road in both drowsy and alert states. The drowsiness criteria are validated with Karolinska Sleepiness Scale (KSS) and video rating based on KSS measurements. The results illustrate that the phase diagram of the steering wheel angle (Ellipse criterion), the standard deviation of the steering wheel angle, and the mean and the standard deviation of the lateral position of the vehicle are highly correlated with drowsiness. The accuracy of the diagnosis was 77% for the Ellipse criterion, 76% for the standard deviation of the steering wheel angle, 67% for the standard deviation of the lateral position, and 65% for the mean value of the lateral position.